There are known semantic networks which are formalisms for knowledge representation in the field of artificial intelligence. A semantic network consists of semantic units and linking objects. The linking objects link respective semantic units and define the type of the link between the respective semantic units. However, it is not possible to expand, delete or amend the knowledge which is present in the semantic units and the linking objects of the semantic network.
From WO 01/45033 A1 there is known a computer-implemented method for processing data structures using a semantic network. Processing objects comprising algorithms and execution controls act on semantic units to which there is a link. Processing objects can be linked to a class object to thereby be able to perform local adaptive processing. The processing objects can use a plurality of algorithms.
According to the aforementioned document there is used a new approach for object-oriented data analysis and especially picture analysis. The main difference of this method is that compared with pixel-oriented picture analysis classification of object primitives is performed. These object primitives are generated during segmentation of the picture. For this purpose a so-called multi-resolution segmentation can be performed. The multi-resolution segmentation allows for segmentation of a picture in a network of homogenous picture region in each resolution selected by a user. The object primitives represent picture information in an abstract form.
As classified information carriers within a picture object network such object primitives and also other picture objects derived from such object primitives offer several advantages as compared to classified pixel.
In general the semantic network comprises two essential components. The first one is a data object network such as a picture object network and the second one is a class object network. Beside the multi-resolution segmentation there is also the possibility of performing a so-called classification-based segmentation.
As mentioned above, the processing objects can be linked to class objects and therefore knowledge which is present in the semantic network can be expanded, deleted or amended by using the processing objects.
However, there exist several problems. The processing objects perform a local adaptive processing in the semantic network. The important aspects of local adaptive processing are analyzing and modifying objects but also navigating through the semantic network according to linking objects. However, the aspect of navigating is not covered by the aforementioned method.